Abstract: Collaborative filtering recommendation algorithm is one of the most successful technologies in the e-commerce recommendation system.Aiming at the problem that traditional collaborative filtering algorithms generally exist sparseness resistance and extendibility，in this paper，a CF algorithm，alternating-least-squares with weighted-[λ]-regularization（ALS-WR） is described.That is，by using regularization constraint to the traditional matrix decomposition model to prevent model overfitting training data and using alternating-least-squares method to train the decomposition model.The experimental evaluation using two real-world datasets shows that ALS-WR achieves better results in comparison with several classical collaborative filtering recommendation algorithms not only in extendibility but also in sparseness resistance.

Key words: recommended systems, collaborative filtering, matrix decomposition, Alternating Least Square（ALS）, Sigular Value Decomposition（SVD）

摘要： 协同过滤推荐算法是电子商务推荐系统中运用最成功的一种推荐技术。针对目前大多数协同过滤算法普遍存在的可扩展性和抗稀疏性问题，在传统的矩阵分解模型（SVD）的基础上提出了一种带正则化的基于迭代最小二乘法的协同过滤算法。通过对传统的矩阵分解模型进行正则化约束来防止模型过度拟合训练数据，并通过迭代最小二乘法来训练分解模型。在真实的实验数据集上实验验证，该算法无论是在可扩展性，还是在抗稀疏性方面均优于几个经典的协同过滤推荐算法。

关键词: 推荐系统, 协同过滤, 矩阵分解, 迭代最小二乘法（ALS）, 矩阵奇异值分解（SVD）